def __init__(
self,
name="Corr Seo 2016",
fix_params=("om", "beta"),
smooth_type="hinton2017",
recon=None,
smooth=False,
correction=None,
isotropic=True,
poly_poles=(0, 2),
marg=None,
):
self.recon_smoothing_scale = None
if isotropic:
poly_poles = [0]
if marg is not None:
fix_params = list(fix_params)
for pole in poly_poles:
fix_params.extend([f"a{{{pole}}}_1", f"a{{{pole}}}_2", f"a{{{pole}}}_3"])
super().__init__(
name=name,
fix_params=fix_params,
smooth_type=smooth_type,
smooth=smooth,
correction=correction,
isotropic=isotropic,
poly_poles=poly_poles,
marg=marg,
)
self.parent = PowerSeo2016(
fix_params=fix_params,
smooth_type=smooth_type,
recon=recon,
smooth=smooth,
correction=correction,
isotropic=isotropic,
marg=marg,
)
if self.marg:
for pole in self.poly_poles:
self.set_default(f"a{{{pole}}}_1", 0.0)
self.set_default(f"a{{{pole}}}_2", 0.0)
self.set_default(f"a{{{pole}}}_3", 0.0)
def __init__(self,
name="Corr Seo 2016",
recon=False,
smooth_type="hinton2017",
fix_params=("om", "f"),
smooth=False,
correction=None):
self.recon = recon
self.recon_smoothing_scale = None
super().__init__(name=name,
fix_params=fix_params,
smooth_type=smooth_type,
smooth=smooth,
correction=correction)
self.parent = PowerSeo2016(fix_params=fix_params,
smooth_type=smooth_type,
recon=recon,
smooth=smooth,
correction=correction)
import logging
from barry.models import PowerSeo2016
if __name__ == "__main__":
logging.basicConfig(
level=logging.DEBUG,
format="[%(levelname)7s |%(funcName)20s] %(message)s")
logging.getLogger("matplotlib").setLevel(logging.ERROR)
recon = True
model1 = PowerSeo2016(recon=recon, name=f"Seo2016, recon={recon}")
model_smooth = PowerSeo2016(recon=recon,
name=f"Seo2016, recon={recon}",
smooth=True)
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.datasets import DummyPowerSpectrum_SDSS_DR12_Z061_NGC
dataset1 = PowerSpectrum_SDSS_DR12_Z061_NGC(name="Recon mean",
recon=recon,
min_k=0.02,
max_k=0.3,
reduce_cov_factor=30,
step_size=5)
dataset2 = DummyPowerSpectrum_SDSS_DR12_Z061_NGC(
name="Dummy data, real window fn",
min_k=0.02,
max_k=0.25,
dummy_window=False)
dataset3 = DummyPowerSpectrum_SDSS_DR12_Z061_NGC(name="DummyWindowFnToo",
min_k=0.02,
max_k=0.25,
for r in [True, False]:
t = "Recon" if r else "Prerecon"
ls = "-" if r else "--"
d = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, realisation=0)
de = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r,
postprocess=p,
realisation=0)
beutler_not_fixed = PowerBeutler2017(recon=r)
beutler = PowerBeutler2017(recon=r)
sigma_nl = 6.0 if r else 9.3
beutler.set_default("sigma_nl", sigma_nl)
beutler.set_fix_params(["om", "sigma_nl"])
seo = PowerSeo2016(recon=r)
ding = PowerDing2018(recon=r)
noda = PowerNoda2019(recon=r, postprocess=p)
for i in range(999):
d.set_realisation(i)
de.set_realisation(i)
fitter.add_model_and_dataset(
beutler_not_fixed,
d,
name=f"Beutler 2017 {t}, mock number {i}",
linestyle=ls,
color="p",
realisation=i)
fitter.add_model_and_dataset(
r_s = c.get_data()["r_s"]
fitter = Fitter(dir_name)
p = BAOExtractor(r_s)
cs = ["#262232", "#116A71", "#48AB75", "#b7c742"]
for r in [True]:
t = "Recon" if r else "Prerecon"
datae = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, min_k=0.03, max_k=0.30, postprocess=p)
for ls, n in zip(["-", ":"], ["", " (No Poly)"]):
if n:
fix = ["om", "f", "a1", "a2", "a3", "a4", "a5"]
else:
fix = ["om", "f"]
fitter.add_model_and_dataset(PowerBeutler2017(postprocess=p, recon=r, fix_params=fix), datae, name=f"Beutler 2017{n}", linestyle=ls, color=cs[0])
fitter.add_model_and_dataset(PowerSeo2016(postprocess=p, recon=r, fix_params=fix), datae, name=f"Seo 2016{n}", linestyle=ls, color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(postprocess=p, recon=r, fix_params=fix), datae, name=f"Ding 2018{n}", linestyle=ls, color=cs[2])
fitter.add_model_and_dataset(PowerNoda2019(postprocess=p, recon=r), datae, name=f"Noda 2019", color=cs[3])
sampler = DynestySampler(temp_dir=dir_name, nlive=300)
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
from chainconsumer import ChainConsumer
c = ChainConsumer()
for posterior, weight, chain, evidence, model, data, extra in fitter.load():
c.add_chain(chain, weights=weight, parameters=model.get_labels(), **extra)
c.configure(shade=True, bins=30, legend_artists=True)
from barry.models import PowerSpectrumFit, PowerSeo2016, PowerBeutler2017, PowerDing2018, PowerNoda2019
from barry.samplers import DynestySampler
from barry.fitter import Fitter
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
sampler = DynestySampler(temp_dir=dir_name, nlive=1000)
fitter = Fitter(dir_name)
cs = ["#262232", "#116A71", "#48AB75", "#D1E05B"]
d = PowerSpectrum_DESIMockChallenge_Handshake(min_k=0.005, max_k=0.3, isotropic=False, realisation="data", fit_poles=[0, 2])
fitter.add_model_and_dataset(PowerBeutler2017(isotropic=False), d, name=f"Beutler 2017 Prerecon", color=cs[0])
fitter.add_model_and_dataset(PowerSeo2016(isotropic=False), d, name=f"Seo 2016 Prerecon", color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(isotropic=False), d, name=f"Ding 2018 Prerecon", color=cs[2])
fitter.add_model_and_dataset(PowerNoda2019(isotropic=False), d, name=f"Noda 2019 Prerecon", color=cs[3])
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
import logging
logging.info("Creating plots")
res = fitter.load()
from chainconsumer import ChainConsumer
import copy
sigma_nl = 6.0 if r else 9.3
model.set_default("sigma_nl", sigma_nl)
model.set_fix_params(["om", "sigma_nl"])
fitter.add_model_and_dataset(PowerBeutler2017(recon=r),
d,
name=f"Beutler 2017 {t}",
linestyle=ls,
color=cs[0])
fitter.add_model_and_dataset(
model,
d,
name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}",
linestyle=ls,
color=cs[0])
fitter.add_model_and_dataset(PowerSeo2016(recon=r),
d,
name=f"Seo 2016 {t}",
linestyle=ls,
color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(recon=r),
d,
name=f"Ding 2018 {t}",
linestyle=ls,
color=cs[2])
fitter.add_model_and_dataset(PowerNoda2019(recon=r, postprocess=p),
de,
name=f"Noda 2019 {t}",
linestyle=ls,
color=cs[3])
for r in [False]:
t = "Recon" if r else "Prerecon"
ls = "-" # if r else "--"
d = PowerSpectrum_Beutler2019_Z061_SGC(recon=r, isotropic=False)
# Fix sigma_nl for one of the Beutler models
model = PowerBeutler2017(recon=r, isotropic=False)
sigma_nl = 6.0 if r else 9.3
model.set_default("sigma_nl_par", 14.1)
model.set_default("sigma_nl_perp", 2.64)
model.set_fix_params(["om", "f", "sigma_nl_par", "sigma_nl_perp"])
fitter.add_model_and_dataset(PowerBeutler2017(recon=r, isotropic=False), d, name=f"Beutler 2017 {t}", linestyle=ls, color=cs[0])
fitter.add_model_and_dataset(model, d, name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}", linestyle=ls, color=cs[0])
fitter.add_model_and_dataset(PowerSeo2016(recon=r, isotropic=False), d, name=f"Seo 2016 {t}", linestyle=ls, color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(recon=r, isotropic=False), d, name=f"Ding 2018 {t}", linestyle=ls, color=cs[2])
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
import logging
logging.info("Creating plots")
from chainconsumer import ChainConsumer
c = ChainConsumer()
for posterior, weight, chain, evidence, model, data, extra in fitter.load():
max_k=0.30,
postprocess=p)
for ls, n in zip(["-", ":"], ["", " (No Poly)"]):
if n:
fix = ["om", "f", "a1", "a2", "a3", "a4", "a5"]
else:
fix = ["om", "f"]
fitter.add_model_and_dataset(PowerBeutler2017(postprocess=p,
recon=r,
fix_params=fix),
datae,
name=f"Beutler 2017{n}",
linestyle=ls,
color=cs[0])
fitter.add_model_and_dataset(PowerSeo2016(postprocess=p,
recon=r,
fix_params=fix),
datae,
name=f"Seo 2016{n}",
linestyle=ls,
color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(postprocess=p,
recon=r,
fix_params=fix),
datae,
name=f"Ding 2018{n}",
linestyle=ls,
color=cs[2])
fitter.add_model_and_dataset(PowerNoda2019(postprocess=p, recon=r),
datae,
name=f"Noda 2019",
model.set_default("sigma_nl_perp", 2.64)
model.set_fix_params(["om", "f", "sigma_nl_par", "sigma_nl_perp"])
fitter.add_model_and_dataset(PowerBeutler2017(recon=r,
isotropic=False),
d,
name=f"Beutler 2017 {t}",
linestyle=ls,
color=cs[0])
fitter.add_model_and_dataset(
model,
d,
name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}",
linestyle=ls,
color=cs[0])
fitter.add_model_and_dataset(PowerSeo2016(recon=r, isotropic=False),
d,
name=f"Seo 2016 {t}",
linestyle=ls,
color=cs[1])
fitter.add_model_and_dataset(PowerDing2018(recon=r, isotropic=False),
d,
name=f"Ding 2018 {t}",
linestyle=ls,
color=cs[2])
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():